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(1) Objective: This paper conducts a comparative study on the perceptual value of fabrics dyed with tea leaves and tea stems and explores the changes and differences in the Kansei image of the dyed fabrics to provide practical guidance for the product design dyed of tea leaves and tea stems based on the experimental results. (2) Methods: firstly, an effective dyeing method was selected through literature research to dye the samples. Secondly, Kansei words were collected and screened out, and the SD scale was established for a perceptual experiment. Thirdly, a factor analysis was performed on the quantified perceptual evaluation data to compare the changes in the fabric perceptual value before and after dyeing. A T-test and cluster analysis were used to study the difference between them. Finally, according to the fabric perceptual experience results, the product design was put into practice. (3) Conclusion: the perceptual value of all the fabrics dyed with Oolong tea leaves and tea stems were improved. Except for silk chiffon and Tencel, there was no significant difference between tea stems dyeing and tea leaves dyeing in other fabrics, which shows that tea stems dyeing has great perceptual value and reuse value.

Individual products and models on the market must be specifically differentiated from the rest to meet user demand. In terms of consumer purchasing behaviour, consumers increasingly base their decisions on subjective terms or the impression that the product leaves on them, both in terms of functionality, usability, safety, and price adequacy, and regarding the emotions and feelings that it triggers in them. This demand has lead both Asia and Europe to implement new methodologies to develop new products, such as “emotional design” or Kansei engineering. This paper presents a systematic literature review (SLR) on the most relevant methodologies based on Kansei engineering and their relevant results in the specific discipline of product design, addressing these five questions: (RQ1) How many studies on KE and emotional design are there in the Scopus and Web of Science (WoS) databases from 1995 to February 2021? (RQ2) Which research topics and types of KE are addressed? (RQ3) Who is leading the research on KE and emotional design? (RQ4) What are the benefits and drawbacks of using and applying the methodology? (RQ5) What are the limitations of the current research? We analysed 87 studies focusing on the Kansei methodology used for product design and device technologies (e.g., shape design, actuators, sensors, structure) and aesthetic aspects (e.g., Kansei words selection, the quantification of measured emotions of results, and detected shortcomings), and provided the database with all the collected information. One identified and highlighted sector in the results is the electronic–technological-device sector. Results confirm that this type of methodology has a majority and direct application in these sectors, and they are widely represented in the automotive and electronics industries. Lastly, this SLR provides researchers with a guide for comparative emotional-design work, and facilitates future designers who want to implement emotional design in their work by selecting the specific type according to the results of the SLR.

Homogeneity of products is a serious problem in China’s solid wood furniture market, especially reflected in the fact that the furniture form cannot meet the individualized demand of consumers. To explore the differences of consumers’ affective response for different styles of solid wood furniture, this study used Kansei engineering to perform perceptual semantic experiments on Ming-style, Qing-style, and modern Chinese-style furniture. The perceptual images of three styles of solid wood chairs were compared by single factor variance. Additionally, it deconstructed the morphological elements of solid wood seats using morphological analysis and established a mapping model between morphological elements and affective response by quantification theory type-I and multivariable linear regression model. The results show that there are differences in affective response between Ming-style, Qing-style, and modern Chinese style solid wood furniture. Qing-style solid wood furniture tends to be “ornate” and “personalized”. Modern Chinese-style solid wood furniture tends to be “modern” and “streamlined”, Ming-style solid wood furniture is in between the two styles. This study can provide furniture designers with a way to compare the differences in affective responses of different products, and the resulting relationship between affective responses and morphological elements can assist in designing products.

Having an accurate understanding of the individual’s Kansei needs and afterwards designing products that match these needs are particularly important in the era of mass personalisation. Although customers’ Kansei needs have been addressed by Kansei engineering, difficulties remain in handling the differences of individual Kansei. In this paper, individual Kansei variance is considered to transform the Kansei words into multisensory design elements, to help designers better understand the individual's Kansei needs. First, a fuzzy cognitive model is proposed to identify the individual Kansei differences in Kansei words by taking customers’ characteristics and purchasing motives into consideration. Second, a fuzzy cognitive model-based mapping method is proposed to interpret Kansei words into multisensory design elements. The method incorporates a fuzzy clustering method and basic-emotion systems to identify Kansei variance and to determine design elements’ membership of Kansei words dynamically. Finally, the prototype application of the proposed method on a compact SUV is illustrated. The results suggest that individual differences in Kansei terms do exist among customers in the same market segment, and the proposed method has good feasibility and practicability in handling individual Kansei differences in emotional design. Those Kansei dimensions that are more prominent in individual Kansei variance are highly recommended for further digging, which would benefit carrying out personalised customisation and differentiated design.

For over three decades, kansei engineering has expanded greatly and has become a significant discipline both in the industrial and the academic worlds. In this paper, I present the current situation of kansei engineering, and plead for the emancipation of other disciplines, as part of kansei research as well. By reconstructing the historical path of kansei research and exploring the variety of disciplines within kansei research, I point out the opportunities for kansei design to emerge. Whereas kansei engineering and kansei science have found their roots in scientifically established approaches (respectively engineering and brain science), kansei design intends to return to earlier Japanese philosophical or cultural works to rediscover the essence of kansei, and to use them as inspirational means for design. This new discipline certainly needs to be elaborated further. Therefore, this paper aims to contribute to the elaboration of a more expansive point-of-view in design research regarding the relationship between human beings and their immediate environment. [PUBLICATION ABSTRACT]

As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI-driven product design methodology with a significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review of big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited to aid product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.

This paper proposes a furniture design method combining Kansei engineering (KE) and shape grammar (SG), to explore how the diagonal ridge elements of classical Suzhou-style buildings can be applied to furniture design and to explain how the styling elements match the cultural imagery in furniture products. Suzhou-style armchairs and cultural elements of diagonal ridges were collected, the most suitable armchairs were selected for incorporating such elements, and their shapes were deconstructed along with characteristic interpretations. A factor bank of diagonal ridge elements was constructed, first through Kansei word selection and evaluation experiments, and then through factor analysis which determined the main cultural elements of the design. The shape grammar theory was applied to design and innovate the selected armchair samples, achieving three design solutions. The solutions were then comprehensively evaluated, and the optimal one was used for the final physical product. The results of the study showed that users had clear subjective feelings about the design incorporating diagonal ridge elements, with their Kansei on three aspects: aesthetic style, decorative complexity, and structural balance. The approach used in this work blends furniture products with cultural imagery on diagonal ridges, providing a feasible methodological reference and an empirical case for cultural sustainability through furniture design.

The design of Chinese-style furniture (CSF) strives to preserve cultural symbols and aesthetic features while enhancing user experience and emotional resonance. This paper explored how to optimize the design of CSF by analyzing the relationships between affective vocabulary, cultural symbols, and user perception, aiming to narrow the gap between designer intent and user perception, thereby enhancing emotional resonance and user satisfaction. An Affinity Diagram was employed to collect and organize affective vocabulary related to furniture design. Subjective weights of the affective vocabulary were calculated using the Analytic Hierarchy Process (AHP), and objective weights were determined through the Entropy Weight (EW) Method. The Coupling Coordination Degree (CCD) Method was applied to identify the core affective vocabulary. The artifact shapes and patterns from the Haihunhou tomb site were analyzed to extract representative design elements. Finally, the Quality Function Deployment (QFD) Method was utilized to examine the mapping relationships between core affective vocabulary and the artifact shapes and patterns, ranking the characteristics of these elements. The optimized design elements were integrated into the design of a Chinese-style chair. The incorporation of cultural symbols and affective vocabulary into Chinese-style chair design enhanced user experience and emotional identification, providing a viable path for innovation in CSF design.

Children’s preferences for the design of consultation room furniture were evaluated with a focus on the emotional and aesthetic factors influencing these preferences. An evaluation model for children’s furniture in consultation rooms was developed using the Scenic Beauty Estimation (SBE) method and Kansei Engineering. Data were collected by gathering children’s aesthetic ratings and emotional assessments of furniture sample images, followed by statistical analysis to identify design preferences on both visual and emotional levels. The SBE results revealed a negative correlation between straight-line desks, backrest-free designs, and square seat surfaces with scenic beauty, suggesting that children prefer furniture with soft, rounded shapes. The Kansei Engineering results identified two principal factors influencing children’s preferences, further highlighting that soft, rounded forms, lightweight and comfortable experiences, and minimalist designs significantly enhance emotional engagement and attraction. The consistency between the two evaluation methods was high. In conclusion, furniture design for children’s consultation rooms should prioritize safety, comfort, and playfulness, incorporating soft, simple, and child-friendly designs to enhance children’s acceptance and comfort within the examination environment.

This study innovatively enhances personalized emotional responses and user experience quality in traditional Chinese folding armchair (Jiaoyi chair) design through an interdisciplinary methodology. To systematically extract user emotional characteristics, we developed a hybrid research framework integrating web-behavior data mining. 1) the KJ method combined with semantic crawlers extracts emotional descriptors from multi-source social data; 2) expert evaluation and fuzzy comprehensive assessment reduce feature dimensionality; 3) random forest and K-prototype clustering identify three core emotional preference factors: \"Flexible Refinement,\" \"Uncompromising Quality,\" and \"ergonomic stability.\" A CNN-GRU-Attention hybrid deep learning model was constructed, incorporating dynamic convolutional kernels and gated residual connections to address feature degradation in long-term semantic sequences. Experimental validation demonstrated the superior performance of our model in three chair design preference prediction tasks (RMSE = 0.038953, 0.066123, 0.0069777), outperforming benchmarks (CNN, SVM, LSTM). Based on the top-ranked preference encoding, we designed a new Jiaoyi chair prototype, achieving significantly reduced prediction errors in final user testing (RMSE = 0.0034127, 0.0026915, 0.0035955). This research establishes a quantifiable intelligent design paradigm for modernizing cultural heritage through computational design.